Mathematics for Machine Learning PDF Download
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Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
ISBN: 1108569323
Category : Computers
Languages : en
Pages : 392
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Book Description
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
ISBN: 1108569323
Category : Computers
Languages : en
Pages : 392
Get Book
Book Description
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Author: Science Research Associates
Publisher:
ISBN: 9780574280749
Category : Mathematics
Languages : en
Pages : 46
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Book Description
Author: Science Research Associates
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 116
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Author: Science Research Associates
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 172
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Author:
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 616
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Author:
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 362
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Author: M. Vere DeVault
Publisher:
ISBN: 9780574431844
Category : Mathematics
Languages : en
Pages :
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Author:
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 476
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Author:
Publisher:
ISBN: 9780574431813
Category : Mathematics
Languages : en
Pages :
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Author: Science Research Associates
Publisher:
ISBN:
Category : Arithmetic
Languages : en
Pages : 326
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Book Description